Generic Workflow for Classification of Highly Imbalanced Datasets Using Deep Learning

2. The method of claim 1, wherein the DAE comprises a first hidden layer having a different number of neurons than an input layer, and a second hidden layer having a different number of neurons than the first hidden layer.

3. The method of claim 2, wherein the DAE further comprises a third hidden layer having a lower number of neurons than the second hidden layer and having a greater number of neurons than an output layer.

4. The method of claim 1, wherein the data engineering comprises one of reducing a dimensionality of records and expanding a dimensionality of records in the at least a portion of the biased dataset.

5. The method of claim 1, wherein the data engineering comprises scaling feature values of records in the at least a portion of the biased dataset.

7. The method of claim 1, wherein the validation data comprises records of the majority class and records of the minority class.

9. The computer-readable storage medium of claim 8, wherein the DAE comprises a first hidden layer having a different number of neurons than an input layer, and a second hidden layer having a different number of neurons than the first hidden layer.

10. The computer-readable storage medium of claim 9, wherein the DAE further comprises a third hidden layer having a lower number of neurons than the second hidden layer and having a greater number of neurons than an output layer.

11. The computer-readable storage medium of claim 8, wherein the data engineering comprises one of reducing a dimensionality of records and expanding a dimensionality of records in the at least a portion of the biased dataset.

12. The computer-readable storage medium of claim 8, wherein the data engineering comprises scaling feature values of records in the at least a portion of the biased dataset.

14. The computer-readable storage medium of claim 8, wherein the validation data comprises records of the majority class and records of the minority class.

16. The system of claim 15, wherein the DAE comprises a first hidden layer having a different number of neurons than an input layer, and a second hidden layer having a different number of neurons than the first hidden layer.

17. The system of claim 16, wherein the DAE further comprises a third hidden layer having a lower number of neurons than the second hidden layer and having a greater number of neurons than an output layer.

18. The system of claim 15, wherein the data engineering comprises one of reducing a dimensionality of records and expanding a dimensionality of records in the at least a portion of the biased dataset.

19. The system of claim 15, wherein the data engineering comprises scaling feature values of records in the at least a portion of the biased dataset.